Design, build, and maintain scalable data pipelines and infrastructure entirely within GCP
Own data solutions end-to-end, from architecture and design through to production delivery
Build and maintain custom connectors to advertising platforms including Google Ads, Meta (Facebook) Marketing API, and other ad platforms
Design and implement data warehouse structures with a deep focus on performance, scalability and usability
Continuously investigate and evaluate new GCP native tools and services, assessing their fit, building proof of concepts, and driving adoption where it makes sense
Set up and configure orchestration and transformation frameworks (such as Airflow and dbt) within the GCP environment
Collaborate closely with backend engineers to ensure data infrastructure aligns with product and system needs
Contribute to data science initiatives and bring genuine curiosity to analytical use cases
Monitor, troubleshoot, and continuously improve the reliability and performance of existing data systems
Define technical standards and best practices for the data platform
Build and maintain data dashboards and reports to surface insights for stakeholders, primarily using Looker Studio.
Requirements
4+ years of hands-on data engineering experience
Bachelor's degree in Data Engineering, Computer Science, Software Engineering or Information System Engineering - a Data Engineering degree is strongly preferred
Deep expertise in data modeling and data warehouse design
Proven experience in Python and common data engineering frameworks
Strong hands-on experience with Google Cloud Platform, particularly BigQuery, Dataflow, Cloud Composer and related GCP data services
Experience building API connectors and integrations with third-party platforms (advertising APIs such as Google Ads, Meta Marketing API, or similar are a strong advantage)
Solid understanding of distributed systems, data at scale and cloud infrastructure
Ownership mindset - you take responsibility, follow through and hold a high bar for quality
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Python
GCP
Airflow
